
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "145"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 90 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 86 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.160962	-0.777178	1.981759	0.669819	0.371683	11.007173	1.309446	0.621642	0.8050	0.8097	0.2386	24.218295	22.639159
2459593	digital_ok	0.00%	0.00%	0.00%	0.00%	2.15%	4.30%	-0.493031	-0.815087	1.831581	0.459149	-0.081085	0.072494	0.494264	1.005376	0.7407	0.7394	0.2460	2.534432	2.605263
2459592	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	0.203425	-0.687404	0.586892	-0.659582	-1.015104	-0.839178	-1.816182	0.182386	0.0298	0.0300	0.0010	nan	nan
2459591	digital_ok	0.00%	0.00%	0.00%	0.00%	1.62%	0.00%	0.058485	-0.498758	2.818614	0.619389	0.452374	0.151744	0.560322	1.402183	0.6823	0.6959	0.3643	1.671699	1.630285
2459590	digital_ok	0.00%	0.00%	0.00%	0.00%	1.62%	0.00%	-0.023206	-0.144150	2.398035	1.606754	2.216350	2.017196	0.013382	1.238489	0.6863	0.6957	0.3548	1.622027	1.631382
2459589	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.162183	-0.668427	2.056168	1.323524	-0.082659	21.431273	1.192902	1.005440	0.7338	0.7280	0.2724	11.573180	11.395031
2459588	digital_ok	0.00%	0.00%	0.00%	0.00%	2.69%	0.54%	-0.343801	-1.136199	2.503905	1.169129	0.273319	-0.130987	0.223395	0.191390	0.7833	0.7636	0.2229	3.171351	2.727067
2459587	digital_ok	100.00%	-	-	-	100.00%	0.00%	-0.212158	-0.206355	3.655680	1.864541	0.765154	1.373817	-0.301625	1.174920	nan	nan	nan	3.156256	2.330234
2459586	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.150735	1.204003	2.703242	2.354244	4.857964	1.826282	1.420119	3.081808	0.6838	0.6935	0.3439	7.238979	6.388394
2459585	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	8.177895	5.868178	5.342558	4.784664	6.216497	4.815112	0.346638	2.699308	0.0303	0.0301	0.0003	0.000000	0.000000
2459584	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.793200	-0.864547	2.269119	3.390730	10.322280	17.779474	0.005577	0.072782	0.6269	0.6375	0.3578	7.558110	5.447557
2459583	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.652717	0.400773	3.105114	5.089879	0.634478	-0.492579	0.467993	1.407601	0.6857	0.6943	0.3708	0.000000	0.000000
2459582	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.015946	0.548597	2.560384	4.646684	0.623496	-0.798679	-0.115963	1.346882	0.6867	0.6959	0.3759	11.165430	10.059696
2459581	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.086900	0.700155	3.213469	4.837940	1.481953	-0.424940	0.410437	1.672069	0.6887	0.6979	0.3735	5.171864	3.908175
2459580	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.374385	0.408542	2.774768	4.882182	1.082959	0.040170	0.261256	1.222159	0.6882	0.6964	0.3704	0.000000	0.000000
2459579	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.126763	0.689747	3.732657	6.129066	1.461953	-0.611126	0.187688	2.457330	0.6795	0.6896	0.3773	4.908735	4.728460
2459578	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-1.048383	-1.000569	0.151505	0.017759	-1.068336	-0.887555	0.139677	0.329510	0.0332	0.0363	0.0012	nan	nan
2459577	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.226577	0.963571	2.831406	4.449337	1.474396	1.010285	1.055657	2.251067	0.6841	0.6927	0.3757	6.918546	5.950187
2459576	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.134913	0.884341	2.308980	4.172297	1.606807	0.983187	0.416865	1.729384	0.7082	0.7167	0.3731	7.001802	6.442836
2459575	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.587796	0.083226	1.940650	3.865848	0.905161	4.246932	2.698908	5.034220	0.7733	0.7778	0.3067	11.402375	10.768290
2459574	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.485025	0.717580	2.042393	3.814190	0.032642	0.466073	-0.226078	1.173992	0.7060	0.7163	0.3652	8.436750	7.792035
2459573	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.017219	0.341952	2.643151	5.747737	0.500899	-0.734028	0.647641	2.367591	0.6995	0.7137	0.3751	6.516523	5.204934
2459572	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.00%	-0.377766	0.325582	1.246254	3.401224	0.376603	0.658390	0.461759	2.867300	0.7021	0.7154	0.3753	1.532576	1.537731
2459571	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.777516	-0.922223	-0.329752	0.036461	0.843342	-0.411157	-0.068547	0.665225	0.0322	0.0334	0.0010	nan	nan
2459570	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459569	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.275137	0.207503	2.470557	3.930459	3.335163	4.639453	2.631603	4.978373	0.7889	0.7931	0.2154	6.749651	6.417581
2459566	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.083327	0.453926	2.955970	4.426863	0.520536	0.059216	1.321298	6.555392	0.6922	0.7039	0.3899	8.082621	7.184138
2459565	digital_ok	0.00%	-	-	-	100.00%	0.00%	-0.219847	-0.219847	-0.209612	-0.209612	0.543918	0.543918	0.058520	0.058520	nan	nan	nan	0.000000	0.000000
2459564	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.612834	0.071886	0.753688	1.265925	2.456636	4.560789	1.346944	0.767804	0.7150	0.7174	0.3859	6.900265	5.858573
2459563	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	2.15%	-0.823560	-0.218412	1.533886	1.465666	0.969359	-0.198281	1.741533	1.730808	0.7086	0.7166	0.3801	1.836424	1.755393
2459562	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-0.073424	0.631572	1.289419	2.625824	0.483261	0.549882	0.853145	1.204297	0.7143	0.7222	0.3723	1.978936	1.765989
2459561	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	5.95%	0.052914	0.508248	1.338996	2.436181	0.681258	0.028503	1.132923	1.179235	0.7143	0.7241	0.3701	1.962910	1.813901
2459560	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459559	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.54%	-0.135656	0.462141	1.412193	2.474851	0.718036	0.293629	0.921260	1.122962	0.7112	0.7176	0.3722	1.861063	1.693383
2459558	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.525935	0.136846	2.768444	5.442620	0.444897	-0.104783	0.880140	1.792646	0.7125	0.7167	0.3819	3.895218	3.764563
2459557	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0304	0.0299	0.0005	nan	nan
2459556	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.54%	-0.137754	0.031845	2.942329	2.293191	1.579747	-0.679213	0.746395	0.808714	0.7104	0.7165	0.3816	1.774282	1.677255
2459554	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.7143	0.7219	0.3891	nan	nan
2459553	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.7117	0.7099	0.3768	nan	nan
2459552	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459551	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	-0.082073	0.069124	1.224039	1.382501	0.438533	0.545383	0.888751	0.549893	0.7107	0.7199	0.3824	1.906922	1.651889
2459550	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0295	0.0291	0.0005	nan	nan
2459549	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	3.24%	-0.189848	0.002605	1.923792	2.203655	0.681422	-0.899892	3.126592	0.755174	0.7109	0.7194	0.3909	2.039551	1.814299
2459542	digital_ok	0.00%	0.00%	0.00%	0.00%	22.32%	0.00%	0.065909	-0.479127	0.429499	0.834653	0.594291	1.371801	0.887863	2.962730	0.7688	0.7713	0.3365	1.990632	1.681586
2459541	digital_ok	0.00%	1.34%	1.79%	0.00%	21.88%	0.00%	-0.087904	0.254709	1.080089	1.402524	0.238829	1.615955	0.850762	2.500394	0.7603	0.7548	0.3132	1.717208	1.618803
2459540	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.154854	0.666913	0.970752	2.070041	-1.056012	9.628257	1.613592	1.593033	0.7686	0.7662	0.3227	4.488062	3.547557
2459536	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.470007	1.420429	2.990633	6.623713	0.488174	4.998617	3.346315	0.902326	0.7407	0.7413	0.4047	4.514909	3.709799
2459535	digital_ok	0.00%	0.00%	0.00%	0.00%	21.43%	1.79%	0.190693	0.104211	1.010619	1.896543	0.369152	0.151826	1.421830	0.594620	0.7959	0.7852	0.3412	2.115679	1.685810
2459534	digital_ok	100.00%	98.66%	98.66%	0.00%	100.00%	0.00%	2.143736	3.624700	0.290716	0.907552	2.387007	4.332503	9.276024	8.959013	0.0893	0.0821	0.0252	1.197193	1.202006
2459533	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.341844	0.568361	1.977263	3.922935	-0.129281	-0.584802	0.353368	1.945063	0.7374	0.7378	0.3891	5.796546	3.740337
2459532	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.534964	0.786060	2.752675	5.819030	-0.012051	-0.324329	0.424199	0.665478	0.7356	0.7372	0.3922	5.111730	3.743233
2459530	digital_ok	0.00%	0.00%	0.00%	0.00%	5.92%	0.00%	0.264359	0.488946	0.942944	1.797161	0.839079	1.021591	1.813952	0.579555	0.7560	0.7502	0.3840	1.880689	1.662590
2459527	digital_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.001266	0.189311	2.565870	4.571994	2.657547	4.234140	1.720001	0.573518	0.9175	0.9450	0.2646	0.000000	0.000000
2459523	digital_ok	100.00%	-	-	-	100.00%	0.00%	-0.083331	0.387643	3.416097	5.832286	0.965028	2.251143	0.908507	1.378859	nan	nan	nan	8.648962	5.514012
2459522	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.581374	1.403183	2.520188	5.266752	0.489552	0.707457	5.504234	1.099256	0.7230	0.7259	0.3130	7.144584	5.756489
2459513	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.758073	-0.471145	-0.087194	-0.203371	0.628194	-0.879788	1.286568	1.296409	0.0317	0.0316	0.0005	nan	nan
2459508	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.156930	3.304534	3.061954	11.255832	2.363834	7.742301	3.037853	16.350332	0.9315	0.9620	0.1305	0.000000	0.000000
2459505	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.333660	0.670074	1.317168	13.530903	-0.196605	1.315245	4.606508	4.242766	0.1147	0.1337	0.0080	1.268791	1.262617
2459503	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.416113	8.655638	1.595989	17.730129	3.209204	23.709264	3.582680	7.846392	0.8861	0.8820	0.4012	0.000000	0.000000
2459501	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.560753	6.375357	1.412756	13.407496	1.485805	8.784433	1.752383	2.006266	0.1679	0.1519	0.0295	0.000000	0.000000
2459500	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	2.541859	8.631501	2.714697	14.276071	4.937002	12.827461	1.411906	1.902006	0.1900	0.1422	0.0359	0.000000	0.000000
2459499	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	3.335273	10.622264	4.255859	18.129913	4.677703	14.342948	1.654912	9.409976	0.1444	0.1178	0.0152	0.000000	0.000000
2459498	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	2.840907	10.969976	2.749602	15.080265	3.260853	13.270878	0.892736	4.438201	0.1572	0.1247	0.0320	0.000000	0.000000
2459497	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	2.095222	14.384756	0.999612	16.243930	0.927271	17.352352	1.785247	3.867299	0.1853	0.1429	0.0332	0.000000	0.000000
2459496	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	20.047820	22.307270	12.681167	12.111930	23.248118	25.035094	1.639272	5.140171	0.8511	0.8581	0.4470	0.000000	0.000000
2459495	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.458440	11.207262	1.944195	13.360302	2.069509	13.328653	1.784815	3.264573	0.9285	0.9338	0.3567	0.000000	0.000000
2459494	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.434131	12.016213	3.263723	16.128091	2.921286	13.494414	1.254358	5.624266	0.9171	0.9244	0.3728	0.000000	0.000000
2459492	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.117246	3.292272	1.432526	16.997562	-0.275928	1.597851	1.837841	8.744086	0.7665	0.8040	0.3239	0.000000	0.000000
2459491	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.279911	4.132314	1.740042	18.501470	0.283000	3.722028	0.676733	3.789772	0.8016	0.8162	0.2573	0.000000	0.000000
2459490	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.914257	4.210499	1.241142	18.484132	-0.090485	1.071978	0.533043	3.598390	0.7539	0.7954	0.3717	0.000000	0.000000
2459489	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.702786	2.323948	1.773943	6.637550	0.236247	108.583394	0.756868	5.662389	0.7702	0.8078	0.3165	0.000000	0.000000
2459488	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.389829	3.060498	1.825686	15.408741	-0.099573	1.411980	1.410086	7.409027	0.7725	0.8026	0.3459	0.000000	0.000000

Antenna Season Report Notebook¶

Antenna 145 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 145, Node 14:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 145: 2459614

Antenna 145: 2459613

Antenna 145: 2459612

Antenna 145: 2459611

Antenna 145: 2459610

Antenna 145: 2459609

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Antenna 145: 2459522

Antenna 145: 2459513

Antenna 145: 2459508

Antenna 145: 2459506

Antenna 145: 2459505

Antenna 145: 2459503

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶